{"title":"基于天-空-地-海网络的 QoS 保证海洋数据反馈资源管理","authors":"Yuanmo Lin;Zhiyong Xu;Jianhua Li;Jingyuan Wang;Cheng Li;Zhonghu Huang;Yanli Xu","doi":"10.1109/JSYST.2024.3439343","DOIUrl":null,"url":null,"abstract":"More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1741-1752"},"PeriodicalIF":4.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resource Management for QoS-Guaranteed Marine Data Feedback Based on Space–Air–Ground–Sea Network\",\"authors\":\"Yuanmo Lin;Zhiyong Xu;Jianhua Li;Jingyuan Wang;Cheng Li;Zhonghu Huang;Yanli Xu\",\"doi\":\"10.1109/JSYST.2024.3439343\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.\",\"PeriodicalId\":55017,\"journal\":{\"name\":\"IEEE Systems Journal\",\"volume\":\"18 3\",\"pages\":\"1741-1752\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Systems Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10660307/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Systems Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10660307/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Resource Management for QoS-Guaranteed Marine Data Feedback Based on Space–Air–Ground–Sea Network
More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.
期刊介绍:
This publication provides a systems-level, focused forum for application-oriented manuscripts that address complex systems and system-of-systems of national and global significance. It intends to encourage and facilitate cooperation and interaction among IEEE Societies with systems-level and systems engineering interest, and to attract non-IEEE contributors and readers from around the globe. Our IEEE Systems Council job is to address issues in new ways that are not solvable in the domains of the existing IEEE or other societies or global organizations. These problems do not fit within traditional hierarchical boundaries. For example, disaster response such as that triggered by Hurricane Katrina, tsunamis, or current volcanic eruptions is not solvable by pure engineering solutions. We need to think about changing and enlarging the paradigm to include systems issues.